1. Field of the Invention
Aspects of the present invention relate generally to a method for estimating more accurately click v. bid forecasting landscapes in a sponsored search scenario.
2. Description of Related Art
As is known in the art, Generalized Second Price (GSP) auctions are commonly used to sell Internet advertising spots against search engine queries. When a user enters a search query in a search engine, the search engine generally returns both query results and sponsored search results (i.e., advertisements intended to be relevant to the query). Advertisers target their ads based on keywords, phrases, and combinations thereof. When a user clicks on a sponsored search result, he is sent to the advertiser's web page, and the advertiser pays the search engine a fee for the referral.
Because the number of ads that the search engine can show to a user is limited, and because different positions on the search results page have different impacts for advertisers (e.g., if two ads are shown together—one above the other—the top ad is usually more likely to be clicked on, etc.), there should exist a system for allocating the positions to advertisers, and auctions have worked well to solve this problem.
For example, under a GSP auction for a specific term, advertisers submit bids stating the maximum amount of money they are willing to pay for a click from the advertisement shown when that term is used by a user in a search; the ad with the highest bid is generally displayed at the top (i.e., it gets the highest “rank”), with the next-highest bid taking up the slot following the highest-bidded advertisement, etc. The “second price” element comes into effect when a user clicks on an advertisement in position k, where position k is not the top position. In such a case, the advertiser in position k is charged, for each click, an amount equal to (or equal plus some nominal amount, such as, for example, one cent) the next highest bid (i.e., the amount bid for the advertisement in position k−1). Given the multiple positions available, GSP “generalizes” the second price auction.
For an advertiser, striking a balance can be difficult because the optimal bid depends on the number and amounts of the other bids for the same term. The problem is compounded by ever-changing probabilities that an advertisement will be clicked on, and limited budgets. Furthermore, if an advertiser opts in to advanced match, the clicks can potentially come from many disparate marketplaces, in some of which the advertiser may have no, or very limited visibility.
Thus, it would be desirable to offer advertisers a means through which they can more accurately predict the ultimate value, or return-on-investment, of their bids.